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This individual's Sleep Quality is generally highest after an average of 3.6 out of 5 of Overall Mood over the previous 24 hours.
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Blue represents the mean of Overall Mood over the previous 24 hours
An increase in 24 hours cumulative Overall Mood is usually followed by an increase in Sleep Quality. (R = 0.646)
Typical values for Sleep Quality following a given amount of Overall Mood over the previous 24 hours.
Typical Overall Mood seen over the previous 24 hours preceding the given Sleep Quality value.
This chart shows how your Overall Mood changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Overall Mood on each day of the week.
This chart shows the typical value recorded for Overall Mood for each month of the year.
This chart shows how your Sleep Quality changes over time.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Sleep Quality on each day of the week.
This chart shows the typical value recorded for Sleep Quality for each month of the year.

Abstract

This individual's Sleep Quality is generally 19% higher than normal after an average of 3.625 out of 5 Overall Mood over the previous 24 hours. This individual's data suggests with a high degree of confidence (p=2.0792378219513E-34, 95% CI 0.511 to 0.781) that Overall Mood has a strongly positive predictive relationship (R=0.65) with Sleep Quality. The highest quartile of Sleep Quality measurements were observed following an average 3.61 out of 5 Overall Mood. The lowest quartile of Sleep Quality measurements were observed following an average 2.6398213750323 /5 Overall Mood.Sleep Quality is generally 11% lower than normal after an average of 2.6398213750323 out of 5 of Overall Mood over the previous 24 hours. Sleep Quality is generally 19% higher after an average of 3.61 out of 5 of Overall Mood over the previous 24 hours.

Objective

The objective of this study is to determine the nature of the relationship (if any) between Overall Mood and Sleep Quality. Additionally, we attempt to determine the Overall Mood values most likely to produce optimal Sleep Quality values.

Participant Instructions

Record your Overall Mood daily in the reminder inbox or using the interactive web or mobile notifications.
Get Sleep as Android here and use it to record your Sleep Quality. Once you have a Sleep as Android account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.

Design

This study is based on data donated by one participant. Thus, the study design is consistent with an n=1 observational natural experiment.

Data Analysis

Overall Mood Pre-Processing
Overall Mood measurement values below 1 out of 5 were assumed erroneous and removed. Overall Mood measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Overall Mood so any gaps in data were just not analyzed instead of assuming zero values for those times.
Overall Mood Analysis Settings

Sleep Quality Pre-Processing
Sleep Quality measurement values below 1 out of 5 were assumed erroneous and removed. Sleep Quality measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Sleep Quality so any gaps in data were just not analyzed instead of assuming zero values for those times.
Sleep Quality Analysis Settings

Predictive Analytics
It was assumed that 0 hours would pass before a change in Overall Mood would produce an observable change in Sleep Quality. It was assumed that Overall Mood could produce an observable change in Sleep Quality for as much as 1 days after the stimulus event.
Predictive Analysis Settings

Data Quantity
13635 raw Overall Mood measurements with 1192 changes spanning 2522 days from 2012-05-06 to 2019-04-01 were used in this analysis. 635 raw Sleep Quality measurements with 312 changes spanning 2520 days from 2012-05-06 to 2019-04-01 were used in this analysis.

Data Sources

Overall Mood data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.

Sleep Quality data was primarily collected using Sleep as Android. Smart alarm clock with sleep cycle tracking. Wakes you gently in optimal moment for pleasant mornings.

Limitations

As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply causation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

The list of the criteria is as follows:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a strongly positive relationship between Overall Mood and Sleep Quality

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 299 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Overall Mood values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Specificity
Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

Temporality
The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

Plausibility
A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Overall Mood and Sleep Quality is coincidental.

Coherence
Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

Experiment
All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

Analogy
The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Overall Mood
Effect Variable Name Sleep Quality
Sinn Predictive Coefficient 0.4178
Confidence Level high
Confidence Interval 0.13464637685126
Forward Pearson Correlation Coefficient 0.646
Critical T Value 1.646
Average Overall Mood Over Previous 24 hours Before ABOVE Average Sleep Quality 3.61 out of 5
Average Overall Mood Over Previous 24 hours Before BELOW Average Sleep Quality 2.64 out of 5
Duration of Action 24 hours
Effect Size strongly positive
Number of Paired Measurements 299
Optimal Pearson Product 0.71277694368476
P Value 2.0792378219513E-34
Statistical Significance 0.6361
Strength of Relationship 0.13464637685126
Study Type individual
Analysis Performed At 2019-04-04

Overall Mood Statistics

Property Value
Variable Name Overall Mood
Aggregation Method MEAN
Analysis Performed At 2019-04-01
Duration of Action 24 hours
Kurtosis 6.8454669666675
Maximum Allowed Value 5 out of 5
Mean 2.9135 out of 5
Median 3 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 1192
Number of Correlations 4086
Number of Measurements 13635
Onset Delay 0 seconds
Standard Deviation 0.52653757307699
Unit 1 to 5 Rating
UPC 767674073845
Variable ID 1398
Variance 0.2772418158618

Sleep Quality Statistics

Property Value
Variable Name Sleep Quality
Aggregation Method MEAN
Analysis Performed At 2019-04-01
Duration of Action 7 days
Kurtosis 2.7819199718877
Maximum Allowed Value 5 out of 5
Mean 3.3303 out of 5
Median 3.2325582504272 out of 5
Minimum Allowed Value 1 out of 5
Number of Changes 312
Number of Correlations 924
Number of Measurements 635
Onset Delay 0 seconds
Standard Deviation 0.83531375879634
Unit 1 to 5 Rating
UPC 754185214911
Variable ID 1448
Variance 0.69774907563447

Tracking Overall Mood

Record your Overall Mood daily in the reminder inbox or using the interactive web or mobile notifications.

Tracking Sleep Quality

Get Sleep as Android here and use it to record your Sleep Quality. Once you have a Sleep as Android account, you can import your data from the Import Data page. This individual's data will automatically be imported and analyzed.
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https://lh6.googleusercontent.com/-BHr4hyUWqZU/AAAAAAAAAAI/AAAAAAAIG28/2Lv0en738II/photo.jpg Principal Investigator - Mike Sinn